JP2585703B2 - Reactor performance prediction device - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION [Object of the Invention] (Field of Industrial Application) The present invention relates to a reactor performance prediction device used for operation management and operation planning in a nuclear power plant.

(Related Art) Generally, in a nuclear power plant, a reactor operation plan is performed using a reactor performance prediction device provided with a reactor core simulator.

In such a reactor performance prediction device, in order to obtain a required reactor power while observing a specified operation limit value, the position of the control rod, the core cooling material, and the The operating state of the reactor is simulated using the burnup of fuel as a main operating parameter.

(Problems to be Solved by the Invention) The conventional reactor performance prediction device is designed to calculate the thermal power distribution in the reactor core by inputting the operation date, the required core thermal output, the position of the control rod, the core flow rate, etc. The prediction was calculated and the result was output.

By the way, in general, when operating a nuclear power plant,
The value of the electric output in the power system is defined as an operation command.

Therefore, in order to plan the operation of the nuclear power plant,
When the core thermal output is input to the reactor performance prediction device, the operator needs to convert a specified electrical output into a reactor output based on the thermal efficiency of a plant such as a turbine. Further, since the value of the thermal output is output from the reactor performance prediction device, it is necessary to convert the thermal output into the electrical output from the generator and read it.

By the way, the thermal efficiency of the plant, for example, in summer,
1% to 2% because the cooling efficiency of the condenser is lower than in winter
It is known that thermal efficiency varies depending on the season, such as a decrease in thermal efficiency.

Therefore, in order to perform a highly accurate operation plan using the reactor performance prediction device, the above-mentioned thermal efficiency on the operation date must be accurately obtained.

However, in order to obtain the above accurate thermal efficiency,
In addition to collecting long-term operation data of the power plant and making decisions based on them, when using the reactor performance prediction device, based on the determined thermal efficiency,
Conventionally, there is a problem that these operations are troublesome because the conversion between the electric output and the heat output has to be performed as described above.

An object of the present invention is to provide a reactor performance prediction device capable of performing an accurate operation plan without troublesome work.

[Constitution of the Invention] (Means for Solving the Problems) For this purpose, the present invention provides an operation parameter setting means for setting an operation parameter of a nuclear power plant, and an operation state of a nuclear reactor according to the set operation parameter. In a reactor performance prediction apparatus comprising a core simulator for performing prediction of the above, and a display means for displaying the predicted operation state, the core thermal output during actual operation of the nuclear power plant, each data indicating the electrical output A plant data collection unit that collects together with the date, a thermal efficiency learning unit that calculates the thermal efficiency of the nuclear power plant according to the predicted time based on the collected core thermal output and electrical output, and electricity set as the operation parameter. Thermo-electric conversion means for converting the output to a core thermal output based on the calculated thermal efficiency. It is characterized in that to predict the operating conditions of the reactor in accordance with the core thermal power.

(Operation) The thermal efficiency is automatically calculated based on the core thermal output and electrical output during actual operation of the nuclear power plant, so operators can save long-term operation results without having to collect operation data. In addition to obtaining an accurate thermal efficiency based on the above, when an electric output is set as an operation parameter, the electric output is automatically converted into a heat output and the processing is performed. A highly accurate operation plan can be performed without the troublesome work of converting between the electric output and the heat output.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

FIG. 1 shows a block diagram of a core management system according to an embodiment of a reactor performance prediction device of the present invention. This core management system is installed at a nuclear power plant site and has a function of performing core management online. In the figure, a process data input device 1 mainly comprises an analog signal input circuit, inputs detection values from various sensors provided in a plant 2, and collects various data as thermal efficiency observation data. The electronic computer 3 inputs and processes the above-mentioned thermal efficiency observation data collected by the plant data input device 1, and performs a prediction calculation of a heat output distribution and the like in the reactor core.

The electronic computer 3 includes a thermal efficiency data file 4 formed on a nonvolatile memory for storing the thermal efficiency observation data, and a core data file 5 for storing various data relating to the core required for the prediction calculation. The core data file 5 contains nuclear constant data of fuel,
Various data for initial setting of the core state and initial conditions for calculation, such as operating history data such as burn-up and fission product composition, and instantaneous data such as operating parameters and power distribution at that time, are stored. Further, in the core data file 5, a set of calculation results is stored together with operation history dependent data updated by the simulation processing in the electronic computer 3.

In addition, various interactive devices 6 such as an input device 61 for operating the computer 3 and inputting operation parameters, a display device 62 for displaying various information, and a printing device 63 for outputting on recording paper are provided.

Each unit in the electronic computer 3 is formed by software, the plant data input unit 31 inputs thermal efficiency observation data from the process data input device 1, and the thermal efficiency learning unit 32 outputs the thermal efficiency observation data. While storing the data in the thermal efficiency data file 4, the thermal efficiency is calculated based on the data.

The thermal-electrical conversion unit 33, based on the calculated thermal efficiency,
It performs mutual conversion between generator output and furnace output.

The operation parameter setting unit 34 inputs operation parameters input from the input device 61. The core simulator 35 performs a prediction calculation of a heat output distribution and the like in the core based on the input operation parameters.

The operation data output unit 36 outputs the obtained heat output distribution to the display device 62 or the printing device 63 together with the input operation parameters.

With the above configuration, the process data input device 1 collects, as thermal efficiency observation data, each data of the electrical output, the core thermal output, and the turbine condensing inlet seawater temperature from the plant 2 at a constant period of, for example, one hour. Since the core thermal output cannot be directly detected, the temperature and flow rate of the water supplied to the core and the above-mentioned temperature and flow rate exiting the core are actually detected and calculated by calculating the heat balance of the core. Can be

The above three types of data are transferred to the thermal efficiency learning unit 32 via the plant data input unit 31. The thermal efficiency learning unit 32 adds date / time data to the three types of data, creates a set of thermal efficiency observation data Ds as shown in FIG. Store.

FIG. 4B shows thermal efficiency observation data tables T _{1 to} T ₁₂ for storing the respective thermal efficiency observation data Ds in the thermal efficiency data file 4. For example, the thermal efficiency observation data observed during January is shown. Data Ds is thermal efficiency observation data table
To T _1, the thermal efficiency observed data Ds observed in February and so thermal efficiency observed data table T _2, the thermal efficiency observed data table T ₁ through T ₁₂ are respectively stored thermal efficiency observation data Ds for each month You.

Each of the thermal efficiency observation data tables T _{1 to} T ₁₂ is divided into 10 areas with heat output levels of 10 ± 5% to 110 ± 5%, and each area is further divided into No. 1 to No. 10.
It is divided into slots.

The thermal efficiency observation data Ds transferred to the thermal efficiency data file 4 is stored in the area corresponding to the core thermal output.
The data is sequentially stored from the No. 1 slot, and the number of data registered in the area is simultaneously stored. No.1
When the area is filled up to 0 slots,
The thermal efficiency observation data Ds of No. 1 slot with the oldest observation date and time are deleted in order, and new thermal efficiency observation data Ds are sequentially stored therein.

By the way, the seawater temperature changes depending on the season.In this embodiment, when the seawater temperature is equal to or higher than the fixed temperature Ts, the corresponding thermal efficiency observation data Ds is set to summer data, and when the seawater temperature is equal to or lower than the fixed temperature Tw, winter data is set. The constant temperatures Ts and Tw are set in order to distinguish them from each other.

The thermal efficiency learning unit 32 calculates the number Ns of times that the seawater temperature was equal to or higher than the certain temperature Ts and the number Nw of times that the seawater temperature was equal to or less than the certain temperature Tw each time the observed data was input from the computer 3. Counting. And in each month,
Assuming that the total number of inputs is Ni, the summer coefficient ks and the winter coefficient kw
Is calculated as Ks = Ns ÷ Ni Kw = Nw ÷ Ni, and the seasonal coefficient Kn is calculated and stored as Kn = Ks−Kw.

In other words, the summer coefficient ks and the winter coefficient kw indicate the ratio of the seawater temperature at “summer temperature” or “winter temperature” in the total number of observations in the month. Therefore, the season coefficient
Kn is kn = 1 when the month is all “summer temperature”, kn = 0 when the “summer temperature” and “winter temperature” are the same number, and kn = −1 when the month is all “winter temperature”. Becomes

In this way, the thermal efficiency observation data Ds corresponding to a maximum of one year and the seasonal coefficient Kn for each month are stored.

When the operator performs a predetermined start-up operation with the input device 61 to simulate the heat output distribution or the like in the core for the operation plan of the plant 2, the operation parameter setting unit 34 causes the display device 62 to display on FIG. Is displayed as shown in FIG. That is, at the top of the screen,
A display section Ga for inputting a date and time to be predicted, a display section Gb for inputting an electrical output or a furnace output, and a display section Gc for inputting a core flow rate are displayed. A display section Gd for specifying the bar position is displayed.

The operator operates the input device 61 to operate the display units Ga.
While inputting desired data to .about.Gc, the control rod position is designated on the display unit Gd. In this case, as shown in FIG. 4A, the output value corresponding to the display unit Gb can be obtained by adding the letter “T” to the absolute value of the heat output as shown in FIG. Alternatively, the absolute value of the electrical output may be input with the letter “E” added. Further, as shown in FIGS. 3 (c) and 3 (d), the heat output or the electric output may be similarly input with the letter “T” or “E” in a ratio to the rated end force.

In addition, the operation parameters are set to 4 corresponding to the display units Ga to Gd.
Although there are species, one of them can be set as an unknown number and "?" Can be input to instruct a search calculation for the parameter.

Here, it is assumed that the operator has input the absolute value of the electric output as shown in FIG. 4 (b).

By the way, as shown in FIG. 4 (b) or (d), when an electric output is designated, it is necessary to convert the electric output into a heat output.

As described above, the thermal efficiency is higher in winter than in summer. Here, assuming that the electric output is Pe and the core heat output is Pt,
The electric power Pe rises almost linearly with the increase in the core thermal output Pt, as shown by the line W in FIG. 5 in winter and by the line S in summer, respectively. Therefore, in core management, the correlation between the two can be approximated by a temporary equation.

Accordingly, if the coefficients are a to d, the electric power Pe and the core heat output Pt are expressed by the following correlation equation.

Pe = a · Pt + b (1) Pt = c · Pe + d (2) The coefficients a to d are represented by the following equations.

c = 1 / a ... (3), d = -b / a ... (4) Therefore, if the above coefficients a and b are known, the conversion between the electric output Pe and the thermal output Pt can be performed.

In the present embodiment, the coefficient a is calculated based on the core thermal output and the electrical output, which are the actual operation data of the plant 2 in the thermal efficiency observation data Ds stored in the thermal efficiency data file 4.
To b.

In this case, as described above, since the thermal efficiency of the plant changes according to the seawater temperature, the coefficients a and b also change according to the seawater temperature. Therefore, in this embodiment, on the basis of all the heat observation data Ds of the thermal efficiency data file 4, while calculating the year coefficient a ₁ ~b _1, sea water temperature in the heat observation data Ds is, the certain temperature Based on the heat observation data Ds of “summer temperature” which is equal to or more than Ts, the summer coefficients a _{2 to} b ₂ are calculated,
"Winter temperature" where the seawater temperature is equal to or lower than the constant temperature Tw.
Based on the thermal observation data Ds, and to calculate the winter coefficient a ₃ ~b _3.

The thermal efficiency learning unit 32 calculates the coefficients a _{1 to} b ₁ , a _{2 to} b ₂ , and a _{3 to} b ₃ as follows. That is, FIG. 6 (a)
Shows the operation of calculating the year coefficient a ₁ ~b _1, in this case, reads out the total heat observed data Ds of the thermal efficiency data file 4 in the thermal efficiency observed data table T ₁ through T ₁₂ (process 701).
Next, the number of the read thermal observation data Ds is checked (process 702). Here, if there is a sufficient number for performing statistical processing (Y in step 702), the width of the observation area of the core heat output in the read heat observation data Ds is further determined (step 70).
3).

For example, when the core heat output is observed at a width of 20% or more, such as when the core heat output data has a minimum of 80% and a maximum of 100% in the heat observation data Ds ( Y) of the process 703, the coefficients a and b in the equation (1) are calculated by the least squares method using the data of the thermal output and the electrical output in each thermal observation data Ds (process 70).
Four).

Now, for example, if the above data is plotted on the graph with an X mark, as shown in FIG. 7, each data is scattered to some extent, and is shown in FIG. 7 by the calculated coefficients a and b. Such a correlation F is obtained.

Here, observation data that is far away from the obtained correlation F is generated. Such data is caused by factors other than the seawater temperature, for example, a turbine bypass valve open state. Therefore, in order to exclude such data, the correlation F is ±
Data out of the range of 3σ, that is, data d ₁ and d ₂ in the figure are excluded (process 705).

Then, using only the data within the above ± 3σ range,
The coefficients a and b are calculated again (process 706).

On the other hand, when the number of pieces of heat observation data Ds read from the thermal efficiency data file 4 is small and insufficient for statistical processing (N in process 702), the design values assumed at the time of this plant design are represented by coefficients a and b. (Step 707).

When the observation width of the core heat output is less than 20% (N in Step 703), the design value is adopted for the coefficient a in the same manner as described above (Step 708), while the coefficient b is the least squares method. (Step 709).

The coefficients a and b calculated in this manner are set as year-round coefficients a _{1 to} b ₁ (process 701).

Next, as shown in FIG. 6 (b), in summer coefficient a ₂ ~b ₂
Is performed. That is, in this case, among the heat observation data Ds thermal efficiency data file thermal efficiency observed data table T ₁ through T ₁₂ 4, sea water temperature is the predetermined temperature Ts or only thermal observation data Ds for "summer temperature", The data is read out as a summer data set (process 801).

Next, as described above, the number of heat observation data Ds is checked (process 802), and if there is a sufficient number for performing statistical processing (Y in process 802), the core heat output in the heat observation data Ds is further determined. The width of the observation area is determined (step 803).

Here, when the observation is made with a width of 20% or more (processing 80
3), the coefficients a and b are calculated by the least square method using the data of the heat output and the electric output in each heat observation data Ds (process 804).

Next, a correlation F between the thermal output and the electrical output is obtained from the calculated coefficients a and b, and data outside the range of ± 3σ is excluded (step 805). Then, the coefficients a and b are calculated again using only the data within the range of ± 3σ (step 806).

On the other hand, when the number of pieces of heat observation data Ds read from the thermal efficiency data file 4 is small and insufficient for statistical processing (N in processing 802), the year-round coefficients a _{1 to} b calculated above are used.
Let _{1 be the} coefficients a and b found here (process 807).

Moreover, (N of step 803) when the observation width of core thermal power is less than 20%, the coefficient determining the year for coefficients a ₁ here a
(Process 808), the coefficient b is calculated by the least squares method.
Is calculated (step 809).

Thus calculated coefficient a, b and summer coefficient a ₂ ~b ₂ to (operation 810).

Next, as shown in FIG. 6 (c), winter coefficient a ₃ ~b ₃
Is performed. That is, in this case, of the thermal observation data Ds in the thermal efficiency observation data tables T _{1 to} T ₁₂ of the thermal efficiency data file 4, only the thermal observation data Ds of “winter temperature” in which the seawater temperature is equal to or lower than the constant temperature Tw, The data is read out as a winter data set (process 901).

Next, coefficients a and b are calculated based on the data of the thermal output and the electrical output in the read thermal observation data Ds, as in the processes 802 to 809 (process 902). Then, the obtained coefficients a and b are set as winter coefficients a _{3 to} b ₃ (processing 90
3).

The operating parameters inputted by the operator are transmitted from the operating parameter setting unit 34 to the core simulator 35.
The data indicating the date is transferred from the core simulator 35 to the thermoelectric converter 33.

The heat / electricity conversion unit 33 determines the month based on the data,
The seasonal coefficient Kn corresponding to the month is read from the thermal efficiency learning unit 32.

Next, when the season coefficient Kn is Kn ≧ 0, the coefficients a and b are calculated as in the following equation based on the coefficient a ₁ for the whole year and the coefficient a ₂ for the summer season.

a = a ₂ × Kn + a ₁ × (1-Kn) (5) b = b ₂ × Kn + b ₁ × (1-Kn) (6) When the seasonal coefficient Kn is Kn <0, the whole year Based on the coefficient for use a ₁ and the coefficient for winter a ₃ , coefficients a and b are calculated as in the following equation.

a = a ₃ × Kn + a ₁ × (1-Kn) (7) b = b ₃ × Kn + b ₁ × (1-Kn) (8) Therefore, for example, the seawater temperature observed during the month is
For all "summer" temperatures, kn is kn = 1, as described above.
(5), (6) From the above equations, the coefficients a and b are
a = a ₂ and b = b ₂ . In the case of all “winter temperatures”, kn
= −1, the coefficients a,
b is a = a ₃ and b = b ₃ . Further, when “summer temperature” and “winter temperature” are the same number, kn = 0, so that a = a ₁ , b =
b becomes ₁ . In other words, the season of the operating month is determined from the appearance rates of the “summer temperature” and “winter temperature” of the seawater temperature, and the coefficients a and b according to the season are set. .

Now, the operator designates the output value of the operation parameter as an electrical output in the format shown in FIG. 4 (b).

Here, the thermal efficiency learning unit 32 calculates the coefficients c, c from the coefficients a, b calculated by the above equations (5) and (6) or the equations (7) and (8) by the above equations (3) and (4). Find d. Then, the coefficients c and d are applied to the above equation (2), and the above electric output is converted into the core thermal output according to the equation. Next, the calculated core heat output value is transferred to the core simulator 35.

The core simulator 35 predicts and calculates the heat output distribution and the like in the core by a predetermined simulation process based on the core heat output and each operation parameter previously transferred from the operation parameter setting unit 34.

This simulation process is based on a known three-dimensional nuclear thermal-hydraulic coupling calculation method using a first-group coarse lattice diffusion model, which has a relatively short calculation time as a physical model, and has a sufficient response to online operation management. We try to ensure the quality and accuracy. About this calculation method, for example, reference M.
Tuiki etal; “Convergence and Acceleration of Void
Iteration in Boiling Water Reacter Core Calculatio
ns ", Nucl. Sci. Eng., 64, 724-732 (1977).

The data indicating the heat output distribution thus predicted and calculated is transferred from the core simulator 35 to the heat-electricity converter 33.

The thermoelectric converter 33 applies the coefficients a and b calculated by the above formulas (5) and (6) or the formulas (7) and (8) to the formula (1), and calculates the heat by the formula. The output is converted to the above electrical output. Next, the converted electrical output data is transferred to the core simulator 35.

The core simulator 35 transfers the predicted and calculated data such as the heat output distribution and the electric output data to the operation data output unit 36. The operation data output unit 36 displays on the display device 62 a prediction display of a predetermined operation state based on the data, an operation parameter input by the input device 61, and the like.

As a result, the heat output distribution and the like are displayed on the display device 62. At this time, the value of the heat output and the value converted into the electric output are also displayed at the same time.

On the other hand, in the above example, the case where the electric output is designated by the operator as shown in FIG. 4B has been described. However, as shown in FIG. If specified as a ratio to output, the operation parameter setting section
At 34, the electrical output is converted to an absolute value and similarly processed.

Further, as shown in FIG. 4 (a), when the heat output is designated, the conversion process by the thermoelectric converter 33 is not executed, and the core simulator 35
A predetermined simulation process is executed. In addition, in this case, the data such as the heat output distribution that is predicted and calculated is converted into electric output data by the thermoelectric converter 33 and displayed in the same manner as described above. Further, as shown in FIG. 4 (c), when the heat output is specified by a ratio to the rated output,
The operation parameter setting unit 34 converts the heat output into an absolute value and performs the same processing.

On the other hand, as shown in FIG. 4 (e), when "?" Is input as an unknown number, or when other operation parameters in FIG. 3 are input as "?" Calculated by a known method from the critical conditions during furnace operation,
Based on the calculated value, an operation state prediction process is executed.

For example, when an electric output is input as an operation parameter and the date / time is specified as an unknown number, the core simulator 35 searches for a date and time at which the specified electric output is obtained, and executes a predetermined operation state prediction process. I do. In this case, since the thermal efficiency changes with the passage of the month, the above date and time are searched for while calculating the correlation coefficients a and b for each month and converting the electric output to the core heat output. If the core thermal output is specified as unknown, the core simulator
The core thermal output calculated by 35 is converted into an electrical output and displayed on the display device 62.

As described above, according to this embodiment, the plant data input unit 31 collects core thermal output, electrical output, and seawater temperature data from the plant 2 and, together with the date, obtains thermal efficiency observation data Ds corresponding to the past year. Is stored in the thermal efficiency file 4. At this time, the degree of appearance of “summer temperature” and “winter temperature” of the collected seawater temperature was
It is stored as a seasonal coefficient Kn for each month. Then, the thermal efficiency learning unit 32 calculates the thermal efficiency during the actual operation of the plant 2 based on the collected core thermal output and the electrical output, and calculates the thermal efficiency at the predicted operating state of the reactor in the past month in the same month. The calculated thermal efficiency is obtained by correcting the calculated thermal efficiency based on the coefficient Kn.

As a result, the operator is required to have accurate thermal efficiency based on long-term operation results without taking the trouble of collecting operation data.

Further, when an electric output is input as an operation parameter, the electric output is converted into a core heat output based on the predicted thermal efficiency, and an operation state of the reactor is predicted according to the core heat output. The core thermal output, which is the result of the prediction, is converted into an electrical output based on the determined thermal efficiency, and is displayed.Therefore, the operator is required to collect operating data and to convert the electrical output and the thermal output. An operation plan with high accuracy can be performed without troublesome work.

In this case, the operation period of the plant is increased, as the number of thermal efficiency observed data Ds of the thermal efficiency observed data table T ₁ through T ₁₂ increases, accurate coefficients a, b are calculated, enables good operation plan accuracy is .

Next, FIG. 8 shows another embodiment of the core management system. In the figure, the same reference numerals as those in FIG. 1 denote the same blocks, and the difference from FIG.
Reference numeral 33 denotes a point not connected to the core simulator 35 but connected to the operation parameter setting unit 34 and the operation data output unit 36.

In this case, when an electric output is input as an operation parameter by the input device 61, the data of the electric output is transferred from the operation parameter setting unit 34 to the thermoelectric conversion unit 33 and converted into a heat output, and the operation parameter setting is performed. Returned to section 34. Then, the heat output and other operation parameters are transferred to the core simulator 35, and a predetermined operation state prediction process is executed. Further, the data of the heat output distribution in the core, which is the result of the prediction, is transferred to the operation data output unit 36, and further transferred to the heat / electricity conversion unit 33, where it is converted to electric power. The electrical output data is displayed on the display device 62 via the operation data output unit 36.

According to this, the core simulator 35 does not need to perform data input / output processing with the thermoelectric converter 33 as in the case of FIG. In this case, when the electric output of the operation parameter is input and the date is specified as an unknown number, the core simulator 35 needs to perform a process of converting the electric output to the heat output every month, so that the date search is executed. become unable.

In the above embodiment, the plant data during actual operation is observed from the plant 2. However, the process data and design value data of the plant are stored in advance in a data file, and the thermal efficiency is determined based on the data. In such a case, the offline processing may be performed.

Further, the seasonal coefficient Kn is calculated for each month, and when the date on which the operation is predicted is m month d day, the above-mentioned equations (5) to (8) are calculated based on the seasonal coefficient Kn corresponding to m month. Calculates the coefficients a and b used in the above equation (1).
The seasonal coefficient Kn is calculated for each month in the early, middle, late, weekly,
Alternatively, the seasonal coefficients Kn may be calculated for each day, and the coefficients a and b may be calculated based on the seasonal coefficient Kn corresponding to the d day which is the predicted operation day.

The coefficient corresponding to the season a, in order to calculate the b, thermal efficiency observed data Ds of the thermal efficiency observed data table T ₁ through T ₁₂
Are classified according to the seawater temperature, but the coefficients a and b may be calculated simply from the thermal efficiency observation data Ds corresponding to the observed date.

Further, when converting the electric power and the core heat output, a linear equation was used as in the above equations (1) and (2).
Various conversion methods are conceivable, such as using the following formula to improve the accuracy or applying another correlation formula that also uses the furnace pressure and the main steam temperature as parameters.

[Effects of the Invention] As described above, according to the present invention, an operator can obtain accurate thermal efficiency based on long-term operation results without taking the trouble of collecting operation data, and can set the operation parameters as operation parameters. Since the electrical output to be converted to the core thermal output based on the thermal efficiency obtained by calculation, the operator, without the cumbersome work of collecting operating data and converting the electrical output and thermal output, A highly accurate operation plan can be performed.

[Brief description of the drawings]

FIG. 1 is a block diagram of a core management system according to one embodiment of the reactor performance prediction apparatus of the present invention, FIG. 2 (a) is an explanatory diagram of thermal efficiency observation data, and FIG. FIG. 3 is an explanatory view of an observation data table, FIG. 3 is a screen view when operating parameters are input, FIGS. 4 (a) to (e) are explanatory views of an input method of output values, and FIG. FIG. 6 (a) is an operation flowchart showing a whole-year coefficient calculation operation, FIG. 6 (b) is an operation flowchart showing a summer-time coefficient calculation operation, FIG. 6 (c) FIG. 7 is an operation flowchart showing a coefficient calculation operation for winter, FIG. 7 is a graph showing a method of calculating a correlation coefficient by the least squares method, and FIG. 8 is a block diagram showing another embodiment of the core management system. . 1 ... process data input device, 2 ... plant, 3 ...
... Electronic computer, 4 ... Thermal efficiency data file, 5 ... Core data file, 31 ... Plant data input section, 32 ...
… Thermal efficiency learning unit, 33… thermal-electric conversion unit, 34… operating parameter setting unit, 35… core simulator, 36… operating data output unit, 61… input device, 62… display device, 63…
... Printing device.

Claims (2)

(57) [Claims] An operating parameter setting means for setting an operating parameter of a nuclear power plant, a core simulator for predicting an operating state of a nuclear reactor in accordance with the set operating parameter, and displaying the predicted operating state. A reactor performance prediction device comprising: a reactor core thermal output during actual operation of the nuclear power plant; and plant data collecting means for collecting data indicating electric power along with a date; and the collected core thermal output. Thermal efficiency learning means for calculating the thermal efficiency of the nuclear power plant according to the predicted time based on the power output and the electrical output; and heat converting the electrical output set as the operating parameter into a core thermal output based on the calculated thermal efficiency.・ Electric conversion means are provided, and the operation state of the reactor is predicted according to the converted core heat output. Reactor performance prediction apparatus according to claim. 2. The method according to claim 1, wherein the thermal / electrical conversion means converts a core thermal output, which is a prediction result of the core simulator, into an electrical output based on the calculated thermal efficiency. A reactor performance prediction device for displaying the electric output of the reactor.